README.md

---
library_name: diffusers
tags:
- pruna-ai
---

# Model Card for PrunaAI/tiny-stable-diffusion-pipe-smashed

This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.

## Usage

First things first, you need to install the pruna library:

```bash
pip install pruna
```

You can then load this model using the following code:

```python
from pruna import PrunaModel

loaded_model = PrunaModel.from_hub("PrunaAI/tiny-stable-diffusion-pipe-smashed")
```

After loading the model, you can use the inference methods of the original model.

## Smash Configuration

The compression configuration of the model is stored in the `smash_config.json` file.

```bash
{
    "batcher": null,
    "cacher": "deepcache",
    "compiler": null,
    "pruner": null,
    "quantizer": null,
    "deepcache_interval": 2,
    "max_batch_size": 1,
    "device": "cpu",
    "save_fns": [],
    "load_fns": [
        "diffusers"
    ],
    "reapply_after_load": {
        "pruner": null,
        "quantizer": null,
        "cacher": "deepcache",
        "compiler": null,
        "batcher": null
    }
}
```

## Model Configuration

The configuration of the model is stored in the `config.json` file.

```bash
{
    "model_index": {
        "_class_name": "StableDiffusionPipeline",
        "_diffusers_version": "0.33.1",
        "_name_or_path": "/Users/davidberenstein/.cache/huggingface/hub/models--PrunaAI--tiny-stable-diffusion-pipe-smashed/snapshots/1bfc83b7c7f704df99192bbf2d6d2e2172a738c1",
        "feature_extractor": [
            "transformers",
            "CLIPImageProcessor"
        ],
        "image_encoder": [
            null,
            null
        ],
        "requires_safety_checker": true,
        "safety_checker": [
            "stable_diffusion",
            "StableDiffusionSafetyChecker"
        ],
        "scheduler": [
            "diffusers",
            "DDIMScheduler"
        ],
        "text_encoder": [
            "transformers",
            "CLIPTextModel"
        ],
        "tokenizer": [
            "transformers",
            "CLIPTokenizer"
        ],
        "unet": [
            "diffusers",
            "UNet2DConditionModel"
        ],
        "vae": [
            "diffusers",
            "AutoencoderKL"
        ]
    },
    "dtype_info": {
        "dtype": "float32"
    }
}
```

## 🌍 Join the Pruna AI community!

[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.com/invite/rskEr4BZJx)
[![Reddit](https://img.shields.io/reddit/subreddit-subscribers/PrunaAI?style=social)](https://www.reddit.com/r/PrunaAI/)